Data and analytics is a critical part of information and technology that is central to digital business and has been a top CIO investment priority for 12 of the last 13 years. As you can tell from the record breaking attendance at our Data and Analytics Summits around the globe (over 3,800 folks in Dallas last week), interest in D&A is at record levels. But what is D&A?

It is the developments related to this third platform which excites me so. Nodding to the OECD research and the additional insight that explores how innovations evolve and diffuse an industry, and how value and productivity accrues over time (sometimes a long time), it is the idea that enough of the starts have aligned in the area of data and analytics governance that suggests we are a crossroads or pivotal moment.

Master data management is a very specific innovation that emerged some years ago. It was not overly effective – but it did start the ball moving that implied that firms need to externalize data from business applications if they seek to govern it effectively. But MDM tended to become just another monolithic systems, technically and organizationally. Something else was needed.

Over the last 10 years, and more clearly in the last three or four, a number of items have emerged or crystallized that suggest we have reached that tipping point where the stars are aligning; the four conditions for “take off” are forming up. This is what excites me.

Beyond the two extremes of global (i.e., master) data and local (i.e., application) data and its governance, we noted a fluid, dynamic, but complex space in between where various amount of re-use was seen across a large array of applications. This came to be known as “shared data management” but it was not bounded or as easily scoped as global and local data. But the concept made perfect sense and emerged a whole back – known as the three rings of information governance. This technique has since been applied to content governance, records governance, and analytic governance. The technique scales and works for departments, applications and business units. See Design an Effective Information Governance Strategy.

The work itself, related to governing data and analytics, was another dimension that touched on workforce skills and management’s ability to organize more effectively. It was only by being explicit here with the three forms of work, and for establishing and recognizing where this work is most effective across the firm, that the operational side of data and analytics became adaptive and effective. See A Day in the Life of an Information Steward. This was expressed in the realization that the work of governing data consists of three aspects:

Setting policy

Enforcing policy (automatized and business-led resolution work)

Data maintenance (the actual work of correcting the data or policy itself)

Most recently discussions shifted from discrete zones such as master data and/or application data to a broader, more flexible, and more connected and holistic strategy – that we call (for better or worse) a data hub strategy. This finally connects the work of governance to integration (application and data) as well as organizational models. See New Research: Implementing the Data Hub: Architecture and Technology Choices.

The final piece, the most important piece, comes last: How to connect data to outcome since this helps determine where to start (the most important outcome) and how to scale slowly (prioritizing) one outcome at a time. This is what makes the whole thing work for the business, by the business. See What’s in Your Data and Analytics Strategy?.

So these are the stars that aligned – as I noted on stage - and why I am so excited to be at the summit and share our research with everyone. I truly believe that when we master these people, process, data and technology skills and capabilities, an effective data and analytics governance platform will emerge.

As it stands, great success is now without our reach. But until we look at unifying and integrating all policies at one, or at least more than one at a time, the take-off and success will lurch forward one victory at a time.

* These so-called platforms are not just technology platforms – they represent people, process, data and technology. So an overall unified strategy and program model is required to align, link and leverage them. We call that the seven building blocks of data and analytics.